PROFILING - CONCEPTS AND APPLICATIONS

Profiling is an approach to put a label or a set of labels on a subject, considering the characteristics of this subject. The New Oxford American Dictionary defines profiling as: “recording and analysis of a person’s psychological and behavioral characteristics, so as to assess or predict his/her capabilities in a certain sphere or to assist in identifying a particular subgroup of people”. This research extends this definition towards things demonstrating that many methods used for profiling of people may be applied for a different type of subjects, namely things. The goal of this research concerns proposing methods for discovery of profiles of users and things with application of Data Science methods. The profiles are utilized in vertical and 2 horizontal scenarios and concern such domains as smart grid and telecommunication (vertical scenarios), and support provided both for the needs of authorization and personalization (horizontal usage).:The thesis consists of eight chapters including an introduction and a summary. First chapter describes motivation for work that was carried out for the last 8 years together with discussion on its importance both for research and business practice. The motivation for this work is much broader and emerges also from business importance of profiling and personalization. The introduction summarizes major research directions, provides research questions, goals and supplementary objectives addressed in the thesis. Research methodology is also described, showing impact of methodological aspects on the work undertaken. Chapter 2 provides introduction to the notion of profiling. The definition of profiling is introduced. Here, also a relation of a user profile to an identity is discussed. The papers included in this chapter show not only how broadly a profile may be understood, but also how a profile may be constructed considering different data sources. Profiling methods are introduced in Chapter 3. This chapter refers to the notion of a profile developed using the BFI-44 personality test and outcomes of a survey related to color preferences of people with a specific personality. Moreover, insights into profiling of relations between people are provided, with a focus on quality of a relation emerging from contacts between two entities. Chapters from 4 to 7 present different scenarios that benefit from application of profiling methods. Chapter 4 starts with introducing the notion of a public utility company that in the thesis is discussed using examples from smart grid and telecommunication. Then, in chapter 4 follows a description of research results regarding profiling for the smart grid, focusing on a profile of a prosumer and forecasting demand and production of the electric energy in the smart grid what can be influenced e.g. by weather or profiles of appliances. Chapter 5 presents application of profiling techniques in the field of telecommunication. Besides presenting profiling methods based on telecommunication data, in particular on Call Detail Records, also scenarios and issues related to privacy and trust are addressed. Chapter 6 and Chapter 7 target at horizontal applications of profiling that may be of benefit for multiple domains. Chapter 6 concerns profiling for authentication using un-typical data sources such as Call Detail Records or data from a mobile phone describing the user behavior. Besides proposing methods, also limitations are discussed. In addition, as a side research effect a methodology for evaluation of authentication methods is proposed. Chapter 7 concerns personalization and consists of two diverse parts. Firstly, behavioral profiles to change interface and behavior of the system are proposed and applied. The performance of solutions personalizing content either locally or on the server is studied. Then, profiles of customers of shopping centers are created based on paths identified using Call Detail Records. The analysis demonstrates that the data that is collected for one purpose, may significantly influence other business scenarios. Chapter 8 summarizes the research results achieved by the author of this document. It presents contribution over state of the art as well as some insights into the future work planned

Data-driven Market Segmentation in Tourism – Approaches, Changes Over Two Decades and Development Potential

Market segmentation studies have become very common in tourism research. While the majority of studies follow an a priori segmentation approach by profiling certain subgroups of the tourism market that are defined in advance, the popularity of post-hoc, a posteriori or data-driven segmentation approaches has increased dramatically since its introduction into tourism research in the early Eighties. This paper aims at reviewing data-driven segmentation studies conducted in tourism research with respect to the constructs under study and the methodology used, investigating developments over the past 24 years since the introduction of data-driven segmentation into tourism and providing an outlook on directions of further development

Beginning to End Racial Profiling: Definitive Solutions to an Elusive Problem

Remedying an elusive practice such as racial profiling remains a challenging issue for the judiciary and reformers must rely on other avenues for a solution. For example, even where evidence demonstrates that minorities are disproportionately stopped and searched, courts rarely recognize the victim\u27s claim or provide relief. Thus, it is clear that courts will not be the catalysts of change. This Article argues that while courts may be reluctant to provide judicial remedies, police departments themselves should not ignore [minorities\u27] perceptions [of racial discrimination] and should take measures to reduce any possible profiling and increase partnerships with communities. An indication that a police department may be engaging in racial profiling has a detrimental and far-reaching impact not only on the individuals who experience it first-hand, but also on other members of the targeted community. Ultimately, this pernicious practice threatens to undermine legitimacy in law enforcement and the criminal justice system for large segments of society, which impacts society as a whole. [This Article] concludes by suggesting proactive remedies institutions and policymakers should consider to alleviate the tensions between communities and police officers with respect to racial profiling. Data collection efforts are imperative to educating the public and police agencies about racial profiling, but these efforts fall short as a long-term remedy. Therefore, in addition to data collection during traffic stops, this Article proposes several policy solutions that the federal government and state legislatures should implement to address racial profiling within local law enforcement agencies. This abstract has been adapted from the author\u27s introduction

Introducing Data Science to Undergraduates through Big Data: Answering Questions by Wrangling and Profiling a Yelp Dataset

There is an insatiable demand in industry for data scientists, and graduate programs and certificates are gearing up to meet this demand. However, there is agreement in the industry that 80% of a data scientist’s work consists of the transformation and profiling aspects of wrangling Big Data; work that may not require an advanced degree. In this paper we present hands-on exercises to introduce Big Data to undergraduate MIS students using the CoNVO Framework and Big Data tools to scope a data problem and then wrangle the data to answer questions using a real world dataset. This can provide undergraduates with a single course introduction to an important aspect of data science

Network estimation in State Space Model with L1-regularization constraint

Biological networks have arisen as an attractive paradigm of genomic science ever since the introduction of large scale genomic technologies which carried the promise of elucidating the relationship in functional genomics. Microarray technologies coupled with appropriate mathematical or statistical models have made it possible to identify dynamic regulatory networks or to measure time course of the expression level of many genes simultaneously. However one of the few limitations fall on the high-dimensional nature of such data coupled with the fact that these gene expression data are known to include some hidden process. In that regards, we are concerned with deriving a method for inferring a sparse dynamic network in a high dimensional data setting. We assume that the observations are noisy measurements of gene expression in the form of mRNAs, whose dynamics can be described by some unknown or hidden process. We build an input-dependent linear state space model from these hidden states and demonstrate how an incorporated $L_{1}$ regularization constraint in an Expectation-Maximization (EM) algorithm can be used to reverse engineer transcriptional networks from gene expression profiling data. This corresponds to estimating the model interaction parameters. The proposed method is illustrated on time-course microarray data obtained from a well established T-cell data. At the optimum tuning parameters we found genes TRAF5, JUND, CDK4, CASP4, CD69, and C3X1 to have higher number of inwards directed connections and FYB, CCNA2, AKT1 and CASP8 to be genes with higher number of outwards directed connections. We recommend these genes to be object for further investigation. Caspase 4 is also found to activate the expression of JunD which in turn represses the cell cycle regulator CDC2.Comment: arXiv admin note: substantial text overlap with arXiv:1308.359

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

Data processing and computer techniques for marine seismic interpretation

This work is divided into two sections, the first containing results and interpretations from marine seismic reflection profiling performed by Durham University (1972) in a region to the north of the Faeroe Islands, and the second containing theories for the removal of multiple reflection effects from marine seismic records by means of digital data processing techniques. The seismic profiling investigations were carried out to ascertain the geological structure causing the gravity 'low' north of the Faeroes which had previously been proposed by Bott, Browitt and Stacey (1971) to be caused by an infilled valley. Results from the 1972 survey shows that this infilled valley has a limited aerial extent containing relatively large basement undulations. Further work was carried out to obtain information about the sedimentary' sequencies and to try and correlate these with sediments in surrounding regions where data had been obtained by previous workers. The profiling work (1972) indicated three major sequencies within the sedimentary column with an overall thickening of sediments away from the uplifted areas of the Iceland - Faeroe Ridge and Faeroe Islands. The data processing section deals principally with the removal of multiple reflections from marine seismic records. An introduction is given to the basic concepts involved throughout this work, and includes a description of noise theory and types of multiple reflections encountered in marine seismic profiling. Some previous methods for multiple elimination are improved upon and then two new techniques are developed, applied to seismic sections, and finally a comparison made between the techniques used. All programs are written in FORTRAN IV for use on the IBM 360 computer, and for displaying purposes, facilities available with the Durham IBM 1130 plotting system were used

Profiling of white-collar crime perpetrators in the short-term insurance industry in South Africa

In the context of violent crimes and criminal investigations, the effectiveness and proven success of offender or criminal profiling have been well documented. In reference to white-collar crime perpetrators offenders, this is a much less documented topic though. For any organisation to function effectively and be profitable there is huge reliance placed on employees. There is an expectation that the employees will carry out their functions with honesty and integrity while having the employer’s best interests in mind. Recent local and international published fraud surveys reported widely on the growing trend that has become known as the insider threat. This trend relates to the actual occurrence of misconduct by staff members and has increased proportionally over the years, i.e. from 55% in 2010 to a staggering 81% in 2015. The aim of this research was to determine how to profile staff members who commit white-collar crime in the South African short-term insurance industry. In addition, this research also focused on an introduction on the South African short-term insurance industry, as well as the suggested sources to consider when profiling staff as potential white-collar criminal perpetrators and the importance of making use of crime linkage analysis. Results of this research include that the main objective of profiling will at all times be to perform a structured social and psychological assessment of the perpetrator and when conducting the profiling of potential white-collar criminal perpetrators, there are specific offender characteristics to consider, and detailed data will be required pertaining to certain categories.Police PracticeM. Tech. (Forensic Investigations

Sex and gender differences in anticancer treatment toxicity - a call for revisiting drug dosing in oncology.

The practice of oncology has dramatically changed in the last decade with the introduction of molecular tumor profiling into routine tumor diagnostics and the extraordinary progress in immunotherapies. However, there remains an unmet need to explore personalized dosing strategies that take into account the patient's sex to optimize the balance between efficacy and toxicity for each individual patient. In this mini-review, we summarize the evidence on sex differences in toxicity of anticancer therapies and present data on dose reduction and dose discontinuation rates for selected chemotherapies and targeted therapies. Finally, we propose the investigation of body composition (specifically fat free muscle mass) as a viable approach for personalized treatment dosage

Using Efficient Path Profiling to Optimize Memory Consumption of On-Chip Debugging for High-Level Synthesis

High-Level Synthesis (HLS) for FPGAs is attracting popularity and is increasingly used to handle complex systems with multiple integrated components. To increase performance and efficiency, HLS flows now adopt several advanced optimization techniques. Aggressive optimizations and system level integration can cause the introduction of bugs that are only observable on-chip. Debugging support for circuits generated with HLS is receiving a considerable attention. Among the data that can be collected on chip for debugging, one of the most important is the state of the Finite State Machines (FSM) controlling the components of the circuit. However, this usually requires a large amount of memory to trace the behavior during the execution. This work proposes an approach that takes advantage of the HLS information and of the structure of the FSM to compress control flow traces and to integrate optimized components for on-chip debugging. The generated checkers analyze the FSM execution on-fly, automatically notifying when a bug is detected, localizing it and providing data about its cause. The traces are compressed using a software profiling technique, called Efficient Path Profiling (EPP), adapted for the debugging of hardware accelerators generated with HLS. With this technique, the size of the memory used to store control flow traces can be reduced up to 2 orders of magnitude, compared to state-of-the-art